A Novel Genetic Algorithm for Constrained Multimodal Multi-Objective Optimization Problems

This paper proposes a multitasking-based genetic algorithm (MTGA-CMMO) to solve constrained multimodal multi-objective optimization problems (CMMOPs). In MTGA-CMMO, the main task is assisted by two auxiliary tasks to obtain all the feasible Pareto solution sets. The constraint boundaries of auxiliar...

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Veröffentlicht in:Mathematics (Basel) Jg. 13; H. 11; S. 1851
Hauptverfasser: Feng, Da, Liu, Jianchang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2025
Schlagworte:
Algorithms
Analysis
constrained multimodal multi-objective optimization problem
Constraints
environmental selection
evolutionary multitasking
Genetic algorithms
mating selection
Methods
Multiple objective analysis
Multitasking
Objectives
Optimization algorithms
Pareto optimization
Pareto optimum
New York
ISSN:2227-7390, 2227-7390
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Zusammenfassung:This paper proposes a multitasking-based genetic algorithm (MTGA-CMMO) to solve constrained multimodal multi-objective optimization problems (CMMOPs). In MTGA-CMMO, the main task is assisted by two auxiliary tasks to obtain all the feasible Pareto solution sets. The constraint boundaries of auxiliary task 1 are dynamically adjusted, facilitating the main task’s population in crossing infeasible regions early in the evolution and providing more evolutionary direction later in the evolution. Auxiliary task 2 can contribute to the exploitation ability of the main task. Meanwhile, a probability-based leader mating selection mechanism is devised to improve the global search capability of MTGA-CMMO. Additionally, three environmental selection strategies are designed to correspond to the different tasks in MTGA-CMMO. Extensive experimental verification demonstrates that MTGA-CMMO outperforms other comparative algorithms across multiple test instances and one practical application problem.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math13111851